3 Phase IConducted to determine toxicity for the dosing of the new interventionFirst time the drug is tested in humansSmall number of patients, 20 to 50Depending on the nature of the new drug, patients are usually healthy volunteersA higher dose is assumed to be more effectiveGoal is to find maximum tolerable dose (MTD)

5 Standard DesignPatients are assigned to dose levels according to predefined rulesAllow for only escalation and de-escalation of doseDoses selected such that, D1,…, DK would be close to MTDMTD is determined statistically as the dose at which 1/3 of the subjects develop toxicity

6 Standard Design Subjects are randomizedThe number of subjects, ri, developing toxicity would be observedpi = ri/ni, is used to calculate the proportions exhibiting toxicityDose-response is modeled based on the probability of toxicityThe MTD would be fitted to this model

7 Standard Design Ethical concerns with the traditional approachPatients might be treated excessively and unnecessarily at low dosesToo many patients may be treated at doses that are too high or too lowHighly likely most subjects are treated at low dosesNot clear that the estimated MTD is the correct dose

8 Adaptive DesignsAdjustments and modifications can be made after the trial has startedDoes not affect the integrity of the trialGoal is to improve upon the probability of success of the trial and correctly identify the clinical benefits of the intervention under investigationProspective adaptations includeStopping a trial early for safety or lack of efficacyDropping the loser - Inferior treatments droppedSample size re-estimation

10 Bayesian Approach Based on Bayes Theorem: Expresses how a subjective degree of belief should rationally change to account for evidenceUsed as a statistical inferential tool in adaptive designsStrength of the Bayesian approachDecision on trial continuation is made as data accumulatesSample size not determined in advance although a maximum size might be specifiedDrawbacksAnalysis after each subject is treated

11 Bayesian ApproachCalculates the predictive probability that the patient will respond to treatmentSpecifies a prior distribution then updates it as information becomes availableUses the likelihood function and the prior distribution to obtain a posterior distributionMTD is determined from the posterior distributionStudies are based on costs and public health benefits

14 Bayesian Approach Once the posterior distribution is calculated:The MTD is revised based on the distribution of aThe mode of the posterior distribution is used to estimate the next doseEach patient is treated at the dose which is closest to the MTDToxicity profile is updated after each patient is treatedThe sequence is repeated until a precise estimate of parameter a is obtained or the sample size is exhausted

15 Traditional vs. BayesianExample: A dose-finding escalation design from an oncology trialTraditional approachThe 3+3 traditional escalation rule (TER)Bayesian approachThe continual reassessment method (CRM)The objective is to determine the MTD for a new drug using the least amount of patients

17 Traditional vs. BayesianSummary of simulation results for the designsMethodAssumed True MTDMean Predicted MTDMean number of PatientsMean number of DLTs3+3 TER10086.714.92.8CRM99.213.415012519.42.914115.52.520016922.418616.82.2

18 Traditional vs. BayesianBoth approaches underestimate the true MTDHowever, the Bayesian approach was much closer to the true value for all dose levelsAt all three dose levels the Bayesian approach required less patientsThe mean number of DLTs for the Bayesian approach was either less than or equal to the traditional approach at all dose levelsThe Bayesian CRM approach proved to be more favorable

19 HybridizationThe Bayesian approach can be used alone or as a hybrid with the classic approachAs a hybrid, the Bayesian approach is used to increase the probability of successExample: Two-arm parallel designCompares a test treatment and a controlUse data from 3 clinical trials with similar sample sizesPrior probabilities for the effect size are 0.1, 0.25, and 0.4 with 1/3 probability for each trial

20 Hybridization The classic approach: The Bayesian approach:Mean of the effect size, = 0.25, is used to calculate the sample size. For the design with β = 0.2:The Bayesian approach:The power of the effect size isΦ is the c.d.f. of the standard normal distributionPrior, π(ε), is the uncertainty of ε, the expected power

21 Hybridization Assuming, one-sided α = 0.025, Pexp =0.66+HybridizationAssuming, one-sided α = 0.025,Pexp =0.66With the hybrid approach the power is less than the 80% power stated in the frequentist approach, recall β = 0.2. In order to reach the expected power of 80%, the sample size needs to be increasedThe Bayesian approach piece is used to increase the probability of success given that the final criterion is p ≤ α = 0.025

22 FDA Guidance – Medical DevicesPrior information and AssumptionsCriterion for success for safety and effectivenessJustification for the proposed sample sizePrior probability of the study claimThis is the probability of the study claim before seeing any new data, and it should not be too highEnsures the prior information does not overwhelm the current data, potentially creating a situation where unfavorable results from the proposed study get masked by a favorable prior distributionProgram Code

23 FDA Guidance – Medical DevicesOperating characteristicsProvide tables of the probability of satisfying the study claim, given “true” parameter values and sample sizes for the new trialProvides an estimate of the probability of a type I error in the case where the true parameter values are consistent with the null hypothesis, or power in the case where the true parameter values are consistent with the alternativeEffective Sample SizeQuantifies the efficiency you are gaining from using the prior information and gauges if the prior is too informative

24 ConclusionBayesian full approach is more beneficial in Phase I studiesInherent adaptive nature of the designConditions are more dynamic than other phases and the flexible nature of the Bayesian approach allows for unexpected changesProduces a posterior probability which is useful in decision making and the transitioning from one phase to the nextDose levels can be modified which could be beneficial for a phase I cancer study

25 ConclusionEven without using a full Bayesian method, hybridization results in increased probability of success in trialsMaintaining the validity and integrity of the study and control of the type I error in applications of the method is importantFeasibility should be evaluated in order to prevent abuse of this method in applications such as endpoints or hypotheses changesThe FDA is cautious of the growing trend of Bayesian designs and continues to set guidelines for its use in Phase I trials